Equine Turnout at St. Lawrence University

Clara Mugnai

Introduction

This project is an exploration of turnout preferences for horses both in the St. Lawrence University riding program and boarding at the school’s barn. Elsa Gunnison Appleton Riding Hall, in Canton, NY is the facility where all of the horses were located and all of the data was gathered. The horses were turned out in rotations based on where they had previously done well outside. They were then observed and various behaviors were recorded.

Some variables of interest in the study were:

  • Time_of_day - the time of day when that observation was recorded
  • Age - the age of the particular horse being observed
  • Personality - based on the four major personalities of horses, this is the personality type of that particular horse
  • Date - the date which the observation was taken
  • Flymask - a 1 if the horse wore a fly mask that day, a 0 if they did not
  • Flysheet - a 1 if the horse wore a fly sheet that day, a 0 if they did not
  • Group - a 1 if the horse was in a group (2+), a 0 if they were not
  • Type_of_turnout - The name of the space the horse was turned out in (Image 1)
  • Frequency_of_trail_swishes - how often the horse swished its tail in turnout (frequent, often, infrequent, or rare)
  • Minutes_grazing - how many minutes of the 30 observed that the horse spent grazing
  • Dialated_nostrils - how many times the horse exhibited dialated nostrils in turnout
  • Rigid_body_posture - how many times the horse exhibited a rigid, tense body posture
  • minutes_spent_pacing - how many minutes of the 30 observed that the horse spent pacing
  • number_whinnies - how many times the horse whinnied in the 30 minutes observed
  • number_positive_interactions_w/friends - refer to Methods
  • number_negative_interactions_w/friends - refer to Methods
  • time_layingdown - minutes spent of the 30 observed, with the horse laying down

There are four different personality types for horses and each type can be grouped into either passive or aggressive (Barteau). Barteau is a U.S. national champion dressage rider, and discusses the four types of horse personalities that are seen in domestic horses. The types are social, fearful, challenging, and aloof. She goes into detail with the characteristics of each type and the “1-10” scale of those personalities. She also states that there is a passive to aggressive scale that applies to each type. Finally, the article goes into how to determine a specific horse’s type and which behaviors and reactions can help to identify the type of personality that is being presented. This was used as a model for how personalities of horses within this study were determined.

An article by Foster also identifies horse personalities, the same four types as Barteau. The main focus of Foster’s article is the way horses express discomfort with minimal movement. She identifies that the eyes and other facial indicators are the most informative signals and that changes in body posture and natural movement are other signals to how a horse feels in turnout (2019). She also discusses that certain horses have different baselines, and these behaviors are universally signals of discomfort.

Foster’s article was used heavily as a way to determine what should be taken into account when observing horses. There were certain accommodations made for the fact that there was a single researcher doing all of the observations, specifically the times of day and the scale for the number of tail swishes. The outside research completed by others, as well as personal experience by the researcher, weighed into the way the numerical scales were done. There was more emphasis on if a horse was grazing or calmly moving around the space than on how many times something alerted them. The overall way they behave in turnout better indicates the contentedness of the horse than small moments of spooking or alarm (Foster, Barteau).

Methods

Site Map: Elsa Gunnison Appleton Riding Hall

Image 1: layout of turnout spaces at St. Lawrence Universities, Elsa Gunnison Appleton Riding Hall

Horses were observed for 30-minute intervals in their natural turnout environments and groups. The intervals in which they were observed were randomized as much as possible, as well as the locations they were in. My personal class schedule and the weather dictated which days and times worked to observe in, and I attempted to rotate through the fields the same way the entire time I took observations. I started in the white rings and moved in a clockwise direction around the fields (white rings, round-pens, medicals, hunt field, boys 3, boys 2,etc). The horses take turns going out, at the discretion of the barn manager, and that is not a set schedule so the ones being observed changed each week.

It is important to note that the turnout location of a medical has no grass for them to graze on, only hay put there by humans, so those observations are not well represented by grazing time. During observation time it was noted how many minutes were spent both grazing (a positive behavior) and pacing (a negative behavior), while acknowledging that they could be doing neither of those things. In addition to that, the number of times they whinnied, dilated their nostrils, or had a rigid body posture were noted as negative signs in turnout. For horses both alone and with friends in turnout it was noted how many positive or negative social interactions they had with each other. Positive interactions were things like nuzzling or grooming and negative were running, chasing, or causing one another distress. Horses in solitary turnout could still be bothered or settled down by neighboring horses so they could have positive or negative interactions, as well. Finally, it was noted how often the horses were swishing their tails with the following scale, having both positive and negative impacts:

  • Frequent – almost constant – score 4 negative
  • Often – regularly – score 2 negative
  • Infrequent – some but not overly obvious – score 2 positive
  • Rare – hardly ever – score 4 positive

Note: The scale used for finding these numbers was arbitrary, and chosen for the fact that it made sense given the importance of tail swishing compared to the overall turnout scores for each horse. The total number of tail swishes per horse, per observation session, were not possible to be observed so this scale was chosen instead.

Upon the completion of data collection, overall turnout scores were calculated. This was done by adding the minutes grazing, tail swishing score (if positive), positive friend interactions, and minutes laying down together to be a positive score. Negative scores were; the sum of minutes pacing, tail swishing score (if negative), occurrences of body tension, whinnying, nostril dilation, and negative friend interactions (Foster). Choosing to add these numbers together was a subjective decision because every observation was 30 minutes long and all of the things that indicated a pleasant time for the horse were considered positive, all of the unpleasant things were considered negative (Paddock Anxiety). The positive and negative scores were then added together to give an overall turnout score.

For any horses that appeared 3 or more times in the data set, their observations were included in the final data set. Those horses were each then observed for approximately 10 minutes in the barn to quickly assess their personality types. In those 10 minutes they were exposed to new stimuli in the form of a human, another horse, and a foreign object to see how they reacted. Based on those reactions they were classified as either social, aloof, challenging, or fearful. For each type there was both a passive or aggressive form, depending on how fast or intense the reactions to the stimuli were (Barteau).

Exploratory Analysis

library(tidyverse)
library(ggplot2)
library(readxl)
library(dplyr)
library(lme4)
library(knitr)
library(pander)
library(ggthemes)
library(kableExtra)
library(rmdformats)
library(hms)
turnout_data <- read_excel("SYE data sheet.xlsx",
 col_types = c(
 ))

turnout_data <- turnout_data %>% mutate(Flymask = if_else(Flymask == "x", "Yes","No")) %>% 
  mutate(Flysheet = if_else(Flysheet == "x", "Yes", "No")) %>%
  mutate(Group = if_else(Group == "x", "Yes", "No")) %>%
  replace(is.na(.), "No") %>%  
  mutate(Horse = fct_reorder(Horse, Totalnegativesigns)) %>%
 arrange(Totalnegativesigns) 

Tables

The variable of “Total Negative Signs” will be used instead of “overall” turnout score because it has less bias towards horses in turnout areas with grass.

Average Number of Negative Signs for each Horse
Horse Average Negative Signs
Cortez 0.00
Lucy 0.00
Bling 0.33
Cenvia 0.33
Cheeto 2.67
Juno 3.00
Tucker 3.00
Oscar 5.00
Kirk 5.00
Monroe 5.00
Mouse 5.40
Vox 5.75
Epsom 7.00
Obi 9.33
Patches 14.00
Milo 17.00
Logan 17.00
Henry 21.00
Average Number of Negative Signs for each Personality
Personality Average Negative Signs Sample Size
Challenging - Passive 2.67 3
Aloof - Passive 3.12 17
Social - Passive 3.36 11
Aloof - Aggressive 5.00 3
Challenging - Aggressive 5.75 4
Social - Aggressive 8.60 15
Fearful - Passive 19.00 6

Boxplots

Interesting things happening here but the small number of observations per group must be noted.

This is showing a difference with the biggest takeaway from this plot being that there are two outliers in the “no flymask” category.

There is not a large difference here, with very similar medians for each group. Again, the outliers are the most significant things in this plot. It can be noted that there is more variation in the no flymask category than in the flymask category.

It looks like certain fields have more success but it also needs to be considered which horses go in those fields. For example, the medical paddocks and the roundpens are the closest in proximity to the barn and hold the least relaxed horses. Those fields also have two of the highest negative scores. However, if horses were randomly assigned to fields that would probably not be the case.

Scatterplots

In this section, each point is representing a single observation period. So there are 3-4 points per horse, and 59 points total on each graph.

There does not seem to be a strong relationship between age and the number of negative signs. The slight downward trend may be due to the small sample sizes at the ends of the trend line, in the center where there are more data points there is less of a trend.

There appears to be no trend in total negative signs as the time of day for turnout changes.

There are a lot of different slopes showing here for the different horses, mainly due to the fact that there are only three observations per horse.

Comparing different Slopes in Scatterplots

Exploring multilevel scenarios, it looks like Age and Group may be related, with different slopes for group and non-group, so as age increases in a group the group does better but as age increases in individual horses they do worse.


Flymask also seems to have significant differences in slopes and intercepts.


## $y
## [1] "Total Negative Signs"
## 
## $x
## [1] "Age"
## 
## $title
## [1] "Age vs. Total Negative Signs"
## 
## attr(,"class")
## [1] "labels"

The different personalities definitely are different, and we have seen that each horse is different so we do need to account for the grouped horses. the personalities seem to make a difference in the slopes here but we have to be mindful of the fact that there are very few of each personalities.


Multilevel Unconditional Means Model

Unconditional means model:
  Totalnegativesigns
Predictors Estimates CI p
(Intercept) 6.69 3.66 – 9.71 <0.001
Random Effects
σ2 16.58
τ00 Horse 35.79
ICC 0.68
N Horse 18
Observations 59
Marginal R2 / Conditional R2 0.000 / 0.683

Looking at the unconditional means model, the ICC value is 0.683, with a significant p-value, so this is a multilevel data set and needs to be allowed to have different intercepts for different horses.

Conclusion

Some take-aways from this are that personality type does seem to make difference to horse success in turnout. Flymask use also seems to make a difference to success in turnout. For interactions, it seems like age and group seem to interact with turnout success, as well as ago and flymask.

Limitations to this study are both few observations and a lack of randomization. The modeling would be a lot more significant if there were enough observations to result in significant and meaningful p-values, and if randomization had been more attainable the issues with field type causing a trend would not happen. Those things would make this study a lot more applicable to be used beyond a singular viewing tool, and instead as a predicting model.

Citations

Barteau, Y. (2009, March 5). Understanding horse personalities, part 1: The 4 basic personality types. Dressage Today. Retrieved December 9, 2021, from https://dressagetoday.com/theory/horse-personalities-basic-types.

Chaya, L., Cowan, E., & McGuire, B. (2006). A note on the relationship between time spent in turnout and behavior during turnout in horses (Equus caballus). Applied Animal Behavior Science, 98(1-2), 155–160. https://doi.org/10.1016/j.applanim.2005.08.020

Foster, R. (2019, October 1). Recognizing pain in stoic horses. The Horse. Retrieved December 9, 2021, from https://thehorse.com/113343/recognizing-pain-in-stoic-horses/.

Paddock anxiety: How to help your horse relax. TRTmethod. (2020, October 12). Retrieved December 9, 2021, from https://www.trtmethod.com/separation-anxiety/how-to-deal- with-paddock-anxiety/.